Gender classification is a problem with a variety of practical applications. Face authentication and recognition systems are discussed, generally, by K. Veropoulos, G. Bebis, and M. Webster in “Investigating the impact of face categorization on recognition performance”, International Symposium on Visual Computing (LNCS, vol 3804), December, 2005.
In computer vision, the majority of studies on gender classification are based on face, because visual information from human faces provides important cues for gender classification. A recent study comparing different gender classification approaches using face information was reported by E. Makinen and R. Raisamo in “Evaluation of gender classification methods with automatically detected and aligned faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 30(3), pages 541-547, 2008. A very small number of studies have also investigated the use of modalities other than face, including gait, iris and fingerprint. For example, gait was investigated by C. Shan, S. Gong, and P. W. McOwan in “Learning gender from human gaits and faces”, IEEE Conference on Advanced Video and Signal Based Surveillance, pages 505-510, September 2007. Iris was investigated by V. Thomas, N. V. Chawla, K. W. Bowyer, and P. J. Flynn in “Learning to predict gender from iris images”, IEEE International Conference on Biometrics: Theory, Applications, and Systems, pages 1-5, September 2007. Fingerprint was investigated by A. Badawi, M. Mahfouz, R. Tadross, and R. Jantz in “Fingerprint-based gender classification”, The International Conference on Image Processing, Computer Vision, and Pattern Recognition, June 2006.